DCP-YOLO: A Lightweight Anti-UAV Object Detection Network

LIU Jia; MAO Die; JIANG Zirui; CAO Yihan; CHEN Dapeng

doi:10.11999/JEIT261031

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LIU Jia, MAO Die, JIANG Zirui, CAO Yihan, CHEN Dapeng. DCP-YOLO: A Lightweight Anti-UAV Object Detection Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261031

Citation:

LIU Jia, MAO Die, JIANG Zirui, CAO Yihan, CHEN Dapeng. DCP-YOLO: A Lightweight Anti-UAV Object Detection Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261031

LIU Jia, MAO Die, JIANG Zirui, CAO Yihan, CHEN Dapeng. DCP-YOLO: A Lightweight Anti-UAV Object Detection Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261031

Citation:

LIU Jia, MAO Die, JIANG Zirui, CAO Yihan, CHEN Dapeng. DCP-YOLO: A Lightweight Anti-UAV Object Detection Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261031

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DCP-YOLO: A Lightweight Anti-UAV Object Detection Network

doi: 10.11999/JEIT261031 cstr: 32379.14.JEIT261031

LIU Jia^{1, 2, 3, 4
,},
MAO Die^{1, 2},
JIANG Zirui^{1, 2},
CAO Yihan^{1, 2, 3, 4},
CHEN Dapeng^{1, 2, 3, 4
,
,}

1.
School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
2.
Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot, Nanjing 210044, China
3.
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044, China
4.
Jiangsu Key Laboratory of Big Data Analysis and Intelligent Systems, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (62473200, 62203226), Jiangsu Province Youth Science and Technology Talent Support Project (JSTJ-2024-195), “Qinglan Project” of Jiangsu Province

Received Date: 2026-07-27
Accepted Date: 2026-10-19
Rev Recd Date: 2026-10-19

Available Online: 2026-10-28

Abstract

Abstract

Objective With the rapid development of the low-altitude economy, Unmanned Aerial Vehicles (UAVs) are increasingly used in aerial photography, logistics, environmental monitoring, and emergency rescue. However, unauthorized UAV operations pose growing challenges to public safety and low-altitude airspace management. Vision-based anti-UAV detection has attracted considerable attention because of its relatively low cost, rich visual information, and ease of deployment. Compared with objects in conventional detection tasks, UAV targets in low-altitude surveillance images are often characterized by small sizes, weak textures, substantial pose variations, and complex background interference. Repeated feature downsampling can weaken or eliminate their edge and structural information, resulting in missed detections, false alarms, and inaccurate localization. Furthermore, practical anti-UAV detection systems must balance detection accuracy, computational complexity, and real-time performance under limited hardware resources. Existing lightweight detection networks often struggle to preserve discriminative spatial details while maintaining computational efficiency, particularly when UAV targets occupy only a small proportion of the input image. To address these challenges, a lightweight anti-UAV object detection network, DCP-YOLO, is proposed based on YOLOv26n, a model from the You Only Look Once (YOLO) family. The objective is to improve the feature representation and detection accuracy of tiny UAV targets while maintaining a compact network structure and real-time inference capability. Methods DCP-YOLO is developed by modifying the lightweight YOLOv26n framework to improve the detection performance of tiny UAV targets while maintaining real-time inference capability. The network incorporates three complementary modules. First, a lightweight feature extraction module, DPC3k2, is designed by integrating Depthwise Separable Convolution (DSConv) and Pinwheel-shaped Convolution (PConv) into the backbone network. This module enhances multidirectional feature representation while reducing computational complexity, enabling more discriminative feature extraction for small UAV targets. DSConv reduces redundant computation by separating spatial feature extraction from channel mapping, while PConv uses asymmetrically padded convolutional branches to capture directional edge and contour information. Second, a learnable reverse upsampling module, CUS, is introduced during feature fusion to replace conventional interpolation-based upsampling. By reconstructing high-frequency edge and texture information lost during downsampling, CUS improves feature reconstruction quality and preserves spatial details during cross-scale feature fusion. Third, a contextual attention enhancement module, C2PSCAA, is constructed by incorporating Context Anchor Attention (CAA) into the C2PSA structure. This module is placed at the end of the backbone network to strengthen long-range spatial dependency modeling and multiscale contextual feature representation with limited additional computational overhead. Through the coordinated optimization of lightweight feature extraction, high-frequency detail reconstruction, and contextual attention modeling, DCP-YOLO improves the representation of tiny UAV targets while maintaining low computational complexity and supporting efficient inference. Results and Discussions Experiments are conducted on the DUT Anti-UAV, TIB-Net, and VisDrone2019 datasets to evaluate the detection accuracy, computational efficiency, and generalization capability of DCP-YOLO. The ablation results in Table 2 and Table 4 demonstrate the contributions of DPC3k2, CUS, and C2PSCAA to lightweight feature extraction, high-frequency detail reconstruction, and contextual feature modeling, respectively. On the DUT Anti-UAV dataset, integrating these modules increases precision, recall, mAP@50, and mAP@50-95 to 98.7%, 90.0%, 92.4%, and 61.7%, respectively. The resulting model contains only 2.29 M parameters and achieves an inference speed of 210.71 frames per second (fps). On the TIB-Net dataset, DCP-YOLO achieves an mAP@50 of 91.0% and an mAP@50-95 of 39.0%, representing improvements of 1.2 and 2.6 percentage points over the baseline, respectively. These results confirm the effectiveness and complementary contributions of the three modules under different target scales and background conditions. The visualization results in Fig. 6 further illustrate the improved detection of tiny UAV targets in cluttered scenes, with fewer missed detections and false alarms in the examples shown. The comparison results in Table 5 indicate that DCP-YOLO achieves higher detection accuracy than the evaluated YOLO-series detectors while maintaining low computational complexity and a compact model size. Compared with the baseline YOLOv26n, mAP@50 and mAP@50-95 increase from 90.0% and 59.4% to 92.4% and 61.7%, respectively, corresponding to improvements of 2.4 and 2.3 percentage points. Meanwhile, computational complexity decreases from 5.2 to 5.0 Giga Floating-Point Operations (GFLOPs). The convergence curves in Fig. 8 show that DCP-YOLO maintains higher precision and mAP@50-95 during training. Furthermore, the comparison results in Table 6 demonstrate that the proposed method achieves competitive or superior detection performance compared with recent anti-UAV and small-object detection methods while retaining a lightweight architecture. The detection results in Fig. 7 further illustrate its ability to identify tiny UAV targets against complex backgrounds. The results on VisDrone2019 in Tables 7 and 8 show that DCP-YOLO also improves detection performance while reducing the number of parameters, computational complexity, and model size. Although VisDrone2019 differs substantially from anti-UAV datasets in target categories and imaging viewpoints, DCP-YOLO improves mAP@50-95 by 1.1 percentage points over YOLOv26n, indicating its adaptability to different scene conditions. To evaluate practical deployment feasibility, the models are further tested on an NVIDIA Jetson Orin Nano platform using TensorRT acceleration under a 25 W power setting, as illustrated in Fig. 9. As shown in Table 9, DCP-YOLO achieves an mAP@50 of 94.59%, an mAP@50-95 of 62.70%, and an inference speed of 116.02 fps in 16-bit Floating-Point (FP16) mode, with a memory consumption of 3.00 GB. Although the inference speed is lower than the 181.27 fps achieved by the baseline on the same edge platform, DCP-YOLO maintains real-time inference capability while achieving higher detection accuracy. The reduction in inference speed may be partly attributable to the additional frequency-domain computations required by the CUS module. These results indicate that reductions in theoretical computational complexity do not necessarily translate into corresponding improvements in actual inference speed. Conclusions A lightweight anti-UAV object detection network, DCP-YOLO, is proposed to address the challenges of insufficient small-target feature representation, high-frequency detail loss, and limited computational resources in low-altitude UAV detection. By jointly optimizing lightweight feature extraction, learnable reverse upsampling, and contextual attention modeling, the proposed method improves the detection accuracy of tiny UAV targets while maintaining a compact network structure. Experimental results on the DUT Anti-UAV, TIB-Net, and VisDrone2019 datasets demonstrate the effectiveness of the proposed modules and their applicability under different scene conditions. Compared with the baseline network, DCP-YOLO achieves higher detection accuracy with fewer parameters and lower theoretical computational complexity. Edge deployment experiments further demonstrate that the proposed method maintains real-time inference capability on a resource-constrained platform despite the additional computational overhead associated with feature reconstruction. Nevertheless, detecting extremely small targets under severe occlusion, adverse weather, and low-light conditions remains challenging because of the degradation of discriminative visual information. In addition, the current network primarily relies on single-frame images and does not explicitly exploit temporal information from consecutive frames or complementary information from multiple sensing modalities. Future work will investigate the integration of target tracking, temporal feature modeling, and multimodal perception, together with further optimization of computationally intensive operations and model compression, to improve detection robustness, generalization capability, and deployment efficiency in complex low-altitude environments.
- Small object detection,
- Anti-UAV detection,
- YOLOv26n,
- Lightweight object detection

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References(33)

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